1. Field of the Invention
This invention relates to a defect classification/inspection system for classifying and inspecting a defect of a semiconductor wafer or the like.
2. Description of the Related Art
A semiconductor device is prepared by forming a fine device pattern on a semiconductor wafer. In forming such a device pattern, a dust particle might stick to the surface of the semiconductor wafer or the semiconductor wafer might be scratched, thus causing a defect thereon. The semiconductor device having such a defect generated thereon is a defective device, which lowers the yield.
Therefore, in order to stabilize the yield on the manufacturing line at a high standard, it is preferred to early find a defect generated by a dust particle or scratch, specify the cause of the defect, and take effective measures for the manufacturing equipment and the manufacturing process.
If a defect is found, a defect inspection system is used to inspect and classify the defect and to specify the equipment and process which caused the defect. The defect inspection system identifies the defect by magnifying it, like an optical microscope.
According to a widely used technique for automatic classification of defects of the semiconductor wafers, a defect is located by comparing the image of a defect with a reference image and then the characteristics of the located defect is further compared with a database for classification.
FIG. 1 shows a process of preparing a defect classification database in a conventional defect classification/inspection system.
In the process of preparing the defect classification database, a defect image and a reference image which are picked up by an image pickup unit 201 are saved in a defect image memory 202 and a reference image memory 203, respectively. Of these images, only the images of the defective part are extracted by a defect extracting unit 204 and the characteristics quantity of the size, color and the like of the defect is digitized as defect information by a characteristics extracting unit 205. The digitized defect information is temporarily stored in a pre-classification data memory 206 and is then classified by the operator. The operator classifies each defect from his/her experience and provides a classification code to each group after the classification. The characteristics information of the respective groups having the classification codes provided thereto is saved in a classification result memory 212 as information of classes 1 to N. Characteristics information obtained at a data selecting unit 213 by eliminating redundancy from the information of the classes 1 to N saved in the classification result memory 212 is saved in a database memory 218 as a database.
FIG. 2 shows a process of executing classification in the conventional defect classification/inspection system.
In the process of executing classification, similarly to the process of preparing the database, a defect image and a reference image which are picked up by the image pickup unit 201 are saved in the defect image memory 202 and the reference image memory 203, respectively. Of these images, only the images of the defective part are extracted by the defect extracting unit 204 and the characteristics quantity of the size, color and the like of the defect is digitized as defect information by the characteristics extracting unit 205. After that, at a comparison/classification code providing unit 219, the defect information is compared with characteristics information of the classes 1 to N contained in the database saved in the database memory 218 and is provided with the code of the class having the coincident characteristics. Thus, the classification result is outputted from a classification result output unit 220.
In order to improve the manufacturing equipment and the manufacturing processes on the basis of defects thus found, it is desired to inspect defects on many semiconductor wafers and identify the precise causes of the defects. However, with the design rules for semiconductor wafers growing ever smaller, various types of defects are generated, requiring much more time and labor for carrying out accurate classification.
When the difference in defect characteristics between the defect groups contained in the database is small, the chance of faulty classification increases. In the conventional database preparation process, the operator determines and classifies defects from his/her experience, as shown in FIG. 1. Therefore, the classification requires skills and it is difficult to eliminate the similarity in defect characteristics between the classification groups contained in the database.
In the classification execution process, since all the defects are classified on a single level, the classification accuracy is lowered and classification errors easily occur.